Guy Mathurin Kouamou Ntonfo, M. Frize, E. Bariciak
{"title":"Detection of Necrotizing Enterocolitis in newborns using abdominal thermal signature analysis","authors":"Guy Mathurin Kouamou Ntonfo, M. Frize, E. Bariciak","doi":"10.1109/MeMeA.2015.7145168","DOIUrl":null,"url":null,"abstract":"In this paper we present a novel approach to early diagnosis of Necrotizing Entercolitis in premature newborns. In particular, using an infrared thermal camera, thermal image of newborn abdomen is acquired. Image processing and spatial segmentation are then used to retrieve thermal signature which is represented by a sample distribution of values from an 8-bit grey level color palette. First order statistical features are then extracted from thermal signature and are used into a classifier. Preliminary results are encouraging and show the potential use of the proposed approach for classification between healthy and sick newborns.","PeriodicalId":277757,"journal":{"name":"2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MeMeA.2015.7145168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In this paper we present a novel approach to early diagnosis of Necrotizing Entercolitis in premature newborns. In particular, using an infrared thermal camera, thermal image of newborn abdomen is acquired. Image processing and spatial segmentation are then used to retrieve thermal signature which is represented by a sample distribution of values from an 8-bit grey level color palette. First order statistical features are then extracted from thermal signature and are used into a classifier. Preliminary results are encouraging and show the potential use of the proposed approach for classification between healthy and sick newborns.